Image feature recognition method and apparatus, storage medium, and electronic apparatus

ABSTRACT

This application discloses an image feature recognition method performed at a computing device. The computing device obtains a first neural network model by training parameters in a second neural network model using a first training set and a second training set consecutively, image features of training pictures in the first training set being marked and image features of training pictures in the second training set being not marked. After obtaining a first neural network model, the computing device obtains a recognition request for recognizing an image feature in a target picture. The computing device then recognizes the image feature in the target picture by applying the target picture to the first neural network model. Finally the computing device returns a first recognition result of the first neural network model, the first recognition result indicating an image feature (for example, a pathological feature) recognized in the target picture.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of PCT Application No.PCT/CN2018/115168, entitled “RECOGNITION METHOD AND DEVICE FOR IMAGEFEATURE, STORAGE MEDIUM AND ELECTRONIC DEVICE” filed on Nov. 13, 2018,which claims priority to Chinese Patent Application No. 201711244421.9,entitled “IMAGE FEATURE RECOGNITION METHOD AND APPARATUS, STORAGEMEDIUM, AND ELECTRONIC APPARATUS” filed with the Chinese NationalIntellectual Property Administration on Nov. 30, 2017, all of which areincorporated by reference in their entirety.

FIELD OF THE TECHNOLOGY

This application relates to the field of the Internet, and specifically,to an image feature recognition method and apparatus, a storage medium,and an electronic apparatus.

BACKGROUND OF THE DISCLOSURE

Diabetic retinopathy (DR) is an important manifestation in diabeticmicroangiopathy, is a type of fundus lesion with specific changes, andis one of the severe diabetic complications. DR includes types such asnon-proliferative DR (NPDR for short) (or referred to as a simple DR ora background DR) and proliferative DR (PDR for short). As predicted bythe World Health Organization (WHO), by 2030, a quantity of global DRpatients will increase to 366 million, DR has become one of the fourleading blinding eye diseases, and prevention and treatment of the DRwill become a serious worldwide problem.

Studies have shown that early diagnosis and treatment on DR patients caneffectively prevent loss of vision and blindness, and fundus photographyexamination is a key to prevention and treatment, regular follow-up tofind disease progression, and timely laser intervention for treatment.However, currently, over 50% diabetes patients in the world do notundergo ocular examination in any form, and screening for DR based on afundus image basically relies on visual observation by anophthalmologist.

In a case of mass screening, doctors need to analyze and process a quitelarge volume of data. The manual interpretation method is time-consumingand laborious, and manual screening is impracticable. Moreover, manualscreening is strongly subjective, data analysis is complex and isdifficult to quantify, and it is difficult to achieve quantitativefollow-up.

For a technical problem of low efficiency of screening for DR in therelated art, no effective solution has been provided yet.

SUMMARY

Embodiments of this application provide an image feature recognitionmethod and apparatus, a storage medium, and an electronic apparatus, toresolve at least a technical problem of low efficiency of screening forDR in the related art.

According to an aspect of the embodiments of this application, an imagefeature recognition method is performed at a computing device. Themethod includes: obtaining a first training set and a second neuralnetwork model, image features of training pictures in the first trainingset being marked and the second neural network model includingparameters to be trained; training the parameters of the second neuralnetwork model into a first neural network model using the image featuresof training pictures in the first training set that have been marked;applying a second training set to the first neural network model, imagefeatures of training pictures in the second training set being notmarked, to recognize image features of a subset of the training picturesin the second training set and marks the image features of the subset ofthe training pictures in the second training set accordingly; andupdating parameters of the first neural network model using the imagefeatures of the subset of the training pictures in the second trainingset that have been marked.

According to another aspect of the embodiments of this application, anon-transitory computer readable storage medium is further provided. Thestorage medium stores a plurality of program units that, when executedby a computing device having one or more processors, cause the computingdevice to perform the foregoing image feature recognition method.

According to another aspect of the embodiments of this application, acomputing device is further provided. The computing device includes oneor more processors and one or more memories storing program units that,when executed by the one or more processors, cause the computing deviceto perform the foregoing image feature recognition method.

In the embodiments of this application, the server recognizes an imagefeature in a target picture by using a first neural network model when arecognition request is obtained, and returns a first recognition resultof the first neural network model, the first recognition result beingused for at least indicating an image feature (for example, apathological feature) recognized from the target picture. The foregoingneural network model may exist in a computer device in a form ofsoftware, and rapidly show a recognition result. If a to-be-recognizedpathological feature is DR, a technical problem of low efficiency ofscreening for DR in the related art may be resolved, thereby improvingefficiency of screening for DR.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings described herein are used for providingfurther understanding for this application and constitute a part of thisapplication. Exemplary embodiments of this application and descriptionsthereof are used for explaining this application and do not constitutean improper limitation to this application. In the accompanyingdrawings:

FIG. 1 is a schematic diagram of a hardware environment of an imagefeature recognition method according to an embodiment of thisapplication.

FIG. 2 is a flowchart of an optional image feature recognition methodaccording to an embodiment of this application.

FIG. 3 is a schematic diagram of a hardware environment of an optionalimage feature recognition method according to an embodiment of thisapplication.

FIG. 4 is a schematic diagram of a hardware environment of an optionalimage feature recognition method according to an embodiment of thisapplication.

FIG. 5 is a schematic diagram of a hardware environment of an optionalimage feature recognition method according to an embodiment of thisapplication.

FIG. 6 is a schematic diagram of an optional neural network modelaccording to an embodiment of this application.

FIG. 7 is a schematic diagram of an optional positive sample imageaccording to an embodiment of this application.

FIG. 8 is a schematic diagram of an optional negative sample imageaccording to an embodiment of this application.

FIG. 9 is a schematic diagram of an optional image feature recognitionapparatus according to an embodiment of this application.

FIG. 10 is a structural block diagram of a server according to anembodiment of this application.

DESCRIPTION OF EMBODIMENTS

To make a person skilled in the art better understand solutions of thisapplication, the following clearly and completely describes thetechnical solutions in the embodiments of this application withreference to the accompanying drawings in the embodiments of thisapplication. Apparently, the described embodiments are merely somerather than all of the embodiments of this application. All otherembodiments obtained by a person skilled in the art based on theembodiments of this application without creative efforts shall fallwithin the protection scope of this application.

In the specification, claims, and accompanying drawings of thisapplication, the terms “first”, “second”, and so on are intended todistinguish between similar objects rather than indicating a specificorder. It is understood that the data termed in such a way areinterchangeable in proper circumstances, so that the embodiments of thisapplication described herein can be implemented in other orders than theorder illustrated or described herein. Moreover, the terms “include”,“have” and any other variants mean to cover the non-exclusive inclusion,for example, a process, method, system, product, or device that includesa list of steps or units is not necessarily limited to those expresslylisted steps or units, but may include other steps or units notexpressly listed or inherent to such a process, method, product, ordevice.

According to a first aspect of the embodiments of this application, amethod embodiment of an image feature recognition method is provided.

Optionally, in this embodiment, the image feature recognition method maybe applied to a hardware environment including a server 102 and aterminal 104 shown in FIG. 1. As shown in FIG. 1, the server 102 isconnected to the terminal 104 through a network. The network includes,but is not limited to, a wide area network, a metropolitan area network,or a local area network. The terminal 104 is not limited to a PC, amobile phone, a tablet computer, and the like. The image featurerecognition method in the embodiments of this application may beperformed by the server 102, or may be performed by the terminal 104, ormay be performed by both the server 102 and the terminal 104. The imagefeature recognition method in the embodiments of this applicationperformed by the terminal 104 may alternatively be performed by a clientinstalled in the terminal.

The following describes a full procedure including the method of thisapplication in detail by using performing the method of this applicationon a server as an example:

Step S11: A user terminal 104 captures a picture (that is, a targetpicture) of a pathological part of a target object by using a capturingapparatus 106.

The capturing apparatus may be an independent image capturing device(for example, a camera, an imager based on a radiation principle), ormay be a module integrated on a user terminal.

Step S12: The terminal transmits a recognition request to a server, torequest to recognize an image feature in the target picture, forexample, to analyze and recognize a condition, for example, informationsuch as a pathological part and a disease type, according to an image.

Optionally, the user terminal and the server may be integrated as thesame device, or may be two separate and different devices. The twodevices may exist in the same local area network and communicate witheach other through the local area network; or may exist in differentlocal area networks and communicate with each other through theInternet.

Optionally, the server may exist in a form of a cloud server, and theuser terminal may be any terminal, for example, a mobile phone of auser. The user may transmit a pathological image (the target picture)photographed at any time to the cloud server for diagnosis. The servermay exist in a hospital as an auxiliary condition diagnosis device, andis a good helper for a doctor to diagnose.

Step S13: Recognize and diagnose the condition according to the targetpicture. An implementation may be as follows:

Step S131: Recognize the image feature (for example, the pathologicalfeature) in the target picture by using a first neural network model,the first neural network model being obtained after training a parameterin a second neural network model by using a marked first training setand an unmarked second training set, pathological features of trainingpictures in the first training set being marked, and pathologicalfeatures of training pictures in the second training set being notmarked.

Step S132: Return a first recognition result of the first neural networkmodel, the first recognition result being used for at least indicatingthe pathological feature recognized from the target picture.

Step S14: The server returns the recognition result to the terminal, forthe terminal to represent the recognition result.

Improvements of this application to the full procedure are mainly instep S13 and steps related to step S13, and the improvements aredescribed in detail below with reference to FIG. 2.

FIG. 2 is a flowchart of an optional image feature recognition methodaccording to an embodiment of this application. As shown in FIG. 2, themethod may include the following steps:

Step S202: A server obtains a recognition request, the recognitionrequest being used for recognizing an image feature in a target picture.

Types of pictures captured by a capturing apparatus include, but are notlimited to: a black and white picture or a color picture obtained byphotographing, an image obtained by computed tomography, an imageobtained by positron emission tomography, an image obtained by magneticresonance imaging, an image obtained by medical ultrasonography, and thelike.

The target picture are captured by photographing a suspected lesion partof a target object. The image feature may be a pathological feature, andthe pathological feature is an image feature of the lesion part, forexample, a feature having no retinal neovascularization corresponding toa NPDR area, and a feature having retinal neovascularizationcorresponding to a PDR area.

Step S204: The server recognizes the image feature in the target pictureby using a first neural network model, the first neural network modelbeing obtained by training a parameter in a second neural network modelby using a first training set and a second training set, image featuresof training pictures in the first training set being marked, and imagefeatures of training pictures in the second training set being notmarked; the first neural network model trained by the first training setbeing used for recognizing the image features of the training picturesin the second training set, and the training pictures with the imagefeatures recognized in the second training set being used for continuingto train the first neural network model.

The foregoing second neural network model is a model whose parameter isnot initialized (for example, a deep neural network model). First, thesecond neural network model is trained by using the marked trainingpictures in the first training set, to initialize the parameter of thesecond neural network model, and the parameter is optimized by using theunmarked training pictures in the second training set; and then theforegoing first neural network model is obtained.

If the second neural network model is trained by completely using markedtraining pictures, marking the training pictures is highlytime-consuming because of a relatively large quantity of demandedtraining pictures. However, in the technical solutions of thisapplication, only some training pictures may be marked (that is,pictures in the first training set), and the remaining training picturesare not marked, thereby reducing a workload during model training.

Step S206: The server returns a first recognition result of the firstneural network model, the first recognition result being used for atleast indicating the image feature recognized from the target picture.

The recognition result herein is closely related to the training of theneural network model. For example, if the training pictures includepositive and negative sample pictures of various types of DR, then therecognition result is a specific type of DR or several types of DR, anda specific type of DR corresponds to a corresponding pathologicalfeature (for example, the NPDR corresponds to having no retinalneovascularization, and the PDR corresponds to having retinalneovascularization). For another example, if the training picturesinclude positive and negative sample pictures of various types ofpneumonia, then the recognition result is a type of pneumonia or severaltypes of pneumonia. The training pictures may alternatively be positiveand negative sample pictures of other pathological types, and detailsare not described herein again.

Through step S202 to step S206, the server recognizes the image featurein the target picture by using a first neural network model in a case ofobtaining the recognition request, and returns a first recognitionresult of the first neural network model, the first recognition resultbeing used for at least indicating the image feature recognized from thetarget picture. The foregoing neural network model may exist in acomputer device in a form of software, and rapidly show a recognitionresult. If a to-be-recognized image feature (for example, a pathologicalfeature) is DR, a technical problem of low efficiency of screening forDR in the related art may be resolved, thereby improving efficiency ofscreening for DR.

This embodiment of this application is further described below in detailwith reference to steps shown in FIG. 2.

In the technical solutions provided in step S202, a server obtains arecognition request, the recognition request being used for requestingto recognize an image feature in a target picture.

The method of this application may be applied to scenarios such as ahospital server, an Internet cloud platform, and a local area network.The terminal transmitting the recognition request may directly be aterminal of the foregoing hospital server, a terminal of the Internetcloud platform, or the terminal in the local area network, or may be adevice communicably connected to the hospital server, the Internet cloudplatform, or the local area network. For example:

As shown in FIG. 3, the foregoing terminal may be a user terminal with acamera such as a mobile phone, a tablet computer, or a notebookcomputer. The user terminal may initiate the recognition request bytransmitting a target picture that meets requirements (for example,definition requirements, image size requirements, and photographing aspecified part) to the hospital server, the Internet cloud platform, andthe like.

As shown in FIG. 4, the foregoing terminal may be another device in thehospital (for example, a computer, a mobile phone, or a Doppler imagerused by medical staff) that can be communicably connected to theforegoing hospital server or the Internet cloud platform. The hospitalserver or the Internet cloud platform may provide services for alldoctors in one or more hospitals. A doctor transmits, by using theforegoing device, a target picture to the hospital server or theInternet cloud platform to initiate a recognition request, and then arecognition result is returned to assist the doctors to diagnose.

As shown in FIG. 5, the foregoing terminal may be a camera in a publicspace and can be communicably connected to the foregoing hospital serveror the Internet cloud platform. The camera photographs a specified part(for example, an area at which the eyes are located) of a crowd (thatis, a target object) in real time, and transmits a target pictureobtained by photographing to the hospital server or the Internet cloudplatform to initiate a recognition request.

For the third case, rapid diagnosis can be provided for some epidemicdiseases, a patient in a public space can be located quickly, and aspecific prompt is also provided (to prompt the patient), to produce agood effect of inhibiting spreading of the diseases.

The recognition result in this application corresponds to the firstneural network model, that is, the first neural network model isobtained by training with images of a specific type of pathology, sothat the first neural network model can recognize such type ofpathology.

Optionally, the first neural network model can be trained with images ofseveral types of pathology, so that the first neural network model isenabled to recognize the several types of pathology. An optional neuralnetwork model is shown in FIG. 6, including an input layer, a pluralityof convolutional layers and fully connected layers, and an output layeroutputting a recognition result.

In the technical solutions provided in step S204, the server recognizesan image feature in a target picture by using a first neural networkmodel, the first neural network model being obtained by training aparameter (which may be a fitting parameter in the plurality ofconvolutional layers and fully connected layers) in a second neuralnetwork model by using a first training set and a second training set,image features of training pictures in the first training set beingmarked, and image features of training pictures in the second trainingset being not marked.

The first neural network model trained by the first training set is usedfor recognizing the image features of the training pictures in thesecond training set, and the training pictures with the image featuresrecognized in the second training set is used for continuing to trainthe first neural network model.

Optionally, before the server recognizes the image feature in the targetpicture by using a first neural network model, the server can train thefirst neural network model in advance: training the second neuralnetwork model by using the training pictures in the first training setand the training pictures in the second training set sequentially, andusing the trained second neural network model as the first neuralnetwork model, the training pictures in the second training set used fortraining the second neural network model being marked by using the imagefeatures that are recognized by the second neural network model inadvance.

In the foregoing embodiments, the training, by the server, the secondneural network model by using the training pictures in the firsttraining set and the training pictures in the second training setsequentially, and using the trained second neural network model as thefirst neural network model can be implemented in the following manner.

Step S21: Perform training initialization on the parameter in the secondneural network model by using the training pictures in the firsttraining set, and use the second neural network model after theparameter initialization as a third neural network model.

The first training set may include a training picture of a positivesample and a training picture of a negative sample. An image area atwhich a pathological feature is located and a pathological type of thepathological feature are recognized in the training picture of thepositive sample. A specific image area not including a pathologicalfeature is recognized from the training picture of the negative sample.An optional positive sample image is shown in FIG. 7, and an optionalnegative sample image is shown in FIG. 8.

For example, for DR, two pathological types, namely, NPDR and PDR, canbe recognized. The second neural network model can learn of relatedimage features (image features such as wavelet features and texturalfeatures) of the two pathological types, namely, NPDR and PDR, from thepositive and negative samples. Whether retinal neovascularization occursmay be used as a sign. For example, it can be learned that there is noretinal neovascularization (which can be recognized by using theforegoing image features) in NPDR (or referred to as a simple DR or abackground DR), and there is retinal neovascularization in the PDR.

Optionally, the foregoing pathological types may be divided according torequirements. For example, PDR may be sub-divided into mild NPDR,moderate NPDR, severe NPDR, and PDR; and NPDR and other diseases may bealso sub-divided in a similar manner.

Step S22: Recognize, the image features of the training pictures in thesecond training set by using the third neural network model, and markthe training pictures in the second training set by using secondrecognition results of the third neural network model, the secondrecognition results are used for at least indicating the image featuresrecognized from the training pictures in the second training set.

A sample size of training pictures in the first training set is limited,so that the training effect is unlikely to be good (for example, theparameter in the second neural network model is in under-fitting, ratherthan an optimal value) if the second neural network model is trained byusing only the samples in the first training set. However, increasingthe sample size in the first training set will greatly increase theworkload of manual marking (marking the training pictures), and the lossoutweighs the gain.

To resolve the foregoing problems, a novel training method is providedin this application. The samples are divided into the first training setand the second training set, after training initialization (which equalsto preliminary training) is performed on the parameter in the secondneural network model by using training pictures in the first trainingset, the second neural network model has a preliminary recognitioncapability (which can recognize some image features in images withobvious image features), and image features (that is, pathologicalfeatures) of training pictures in the second training set are recognizedby using the second neural network model having the preliminaryrecognition capability.

Optionally, the marking the training pictures in the second training setby using second recognition results of the third neural network modelmay include: searching all the second recognition results of thetraining pictures in the second training set for a plurality of thirdrecognition results with a highest confidence; and marking correspondingtraining pictures in the second training set by using the thirdrecognition results.

The second recognition results may further be used for indicatingprobabilities that the recognized image features belong to each of aplurality of pathological types, and searching all the secondrecognition results of the training pictures in the second training setfor a plurality of (for example, N) third recognition results with ahighest confidence may be implemented in the following manner.

Step S221: Calculate a confidence s of a second recognition result of acurrent training picture in the second training set according to thefollowing formula:

s=PA*d ^(w) ¹ +w ₂ *v

PA is a parameter that is used for representing importance of thecurrent training picture and that is determined according to aprobability of each type in the second recognition result of the currenttraining picture, d being a density parameter determined according to afeature vector recognized by the third neural network model from thecurrent training picture and a feature vector of the training picturesin the second training set, v being a diversity parameter determinedaccording to the feature vector recognized by the third neural networkmodel from the current training picture and a feature vector of thetraining pictures in the first training set, w₁ and w₂ beingpre-configured parameters.

An optional calculation formula of PA is as follows:

${PA} = {- {\sum\limits_{i = 1}^{N_{c}}\; {p_{i}{\log \left( p_{i} \right)}}}}$

p_(i) represents a probability of belonging to the i^(th) type in N_(c)types, and a value of i ranges from 1 to N_(c).

An optional calculation formula of d is as follows:

$d = {\frac{1}{{M\; 1}}{\sum\limits_{x^{\prime} = 1}^{M\; 1}\; \frac{{f_{FC}(x)}{f_{FC}\left( x^{\prime} \right)}}{{{f_{FC}(x)}}*{{f_{FC}\left( x^{\prime} \right)}}}}}$

A feature vector f_(FC) of a fully connected layer (that is, the fullyconnected layer adjacent to the output layer, or referred to as aclassification layer, in FIG. 6) of a previous layer of an imageclassification layer in a second training set U, a density x, and a meancosine distance of a feature vector corresponding to images in thesecond training set U may be extracted by using a classification model.In the formula, f_(FC)(x) represents a feature vector of a currenttraining picture, f_(FC)(x′) represents a feature vector of trainingpictures in U, M1 represents a quantity of training pictures in thesecond training set U, an operator “∥f_(FC)(x)|” represents a norm ofthe vector f_(FC)(x), and ∥f_(FC)(x′)∥ represents a norm of the vectorf_(FC)(x′) which is actually a mapping from a normed linear space to anon-negative real number. If f_(FC)(x) is a two-dimensional vector, the“∥f_(FC)(x)|” represents a length of the vector f_(FC)(x).

Optionally, the images in U may be clustered according to the featurevector of the fully connected layer by using the K-means algorithm, toobtain K cluster centers. Because any image x in U is clustered, theforegoing f_(FC)(x′) may be replaced with a feature vector of thecluster centers. In this case, M1 is a quantity K of the cluster centers(or clusters). A total quantity of calculations can be greatly reducedby using such a method.

An optional calculation formula of v is as follows:

$v = {{- \frac{1}{{M\; 2}}}{\sum\limits_{x^{\prime} = 1}^{M\; 2}\; \frac{{f_{FC}(x)}{f_{FC}\left( x^{\prime} \right)}}{{{f_{FC}(x)}}*{{f_{FC}\left( x^{\prime} \right)}}}}}$

A feature vector f_(FC) of a fully connected layer (that is, the fullyconnected layer adjacent to the output layer, or referred to as aclassification layer in FIG. 6) of a previous layer of an imageclassification layer in a first training set V, a density x, and a meancosine distance of a feature vector corresponding to an image in thefirst training set V may be extracted by using the classification model.In the formula, f_(FC)(x) represents a feature vector of a currenttraining picture, f_(FC)(x′) represents a feature vector of trainingpictures in V, and M2 represents a quantity of training pictures in thefirst training set V.

Optionally, the images in V may be clustered according to the featurevector of the fully connected layer by using the K-means algorithm, toobtain R cluster centers. Because any image x in V is clustered, theforegoing f_(FC)(x′) may be replaced with a feature vector of thecluster centers. In this case, M2 is a quantity R of the cluster centers(or clusters). A total quantity of calculations can be greatly reducedby using such a method.

Step S222: Obtain a plurality of results with a highest confidence inall the second recognition results of the training pictures in thesecond training set as third recognition results.

In addition to uncertainty, density, and diversity, evaluation indexesof an importance score (the confidence) may be another index of the sameclass, for example, a gradient length and a Fisher information matrix.In addition to measurement based on each picture, the evaluation indexesmay further be used for measurement based on an image set after dataenhancement is performed on each picture.

Step S23: Re-adjust a parameter in the third neural network model bycontinuing to train the third neural network model by using the markedtraining pictures in the second training set, and use the third neuralnetwork model after the parameter re-adjustment as the first neuralnetwork model.

In the technical solution provided in step S206, the server returns afirst recognition result of the first neural network model, the firstrecognition result being used for at least indicating the image featurerecognized from the target picture.

Optionally, the returning a first recognition result of the first neuralnetwork model may include: returning a first recognition result used forrepresenting NPDR; and/or returning a first recognition result used forrepresenting PDR.

Application modes of the technical solutions of this application on aproduct side include, but are not limited to, foreground recognition andbackground recognition. For example, business logic is that a funduspicture of a to-be-predicted patient is transferred to a server on whichthe technical solutions of this application are located for diseaseclassification, and returns a severity degree of the disease (forexample, normal, mild non-proliferative, moderate non-proliferative,severe non-proliferative, or proliferative) according to aclassification result. Such solutions may be directed to a hospital anda private doctor for auxiliary diagnose on DR, or may be directed to anindividual to help the individual for health consultation.

By using the recognition solutions of this application, a rate ofconvergence of a disease diagnosis model (for example, a DRclassification model) can be accelerated, when a same volume of data ismarked, a degree of convergence is improved greatly, and costs of manualmarking are reduced, and when a classification model with the samequality is trained, the volume of data that needs to be manually markedis reduced greatly by using a frame of this application.

In an optional embodiment, details are provided below by using DR as anexample.

In this optional embodiment, when a deep neural network model istrained, the following steps may be cycled until a model converges:

providing an unmarked data set U (the second training set), a markeddata set L (the first training set), and a DR classification model f(that is, the second neural network model), and training f on the markeddata L; predicting data in the unmarked data set U by using f;quantitatively calculating the uncertainty, the density, and thediversity of the data based on a prediction result to obtain a diversityscore (that is, a confidence) of each piece of data in U; and sortingthe data in U according to the diversity score, selecting top N piecesof data to mark the top N pieces of data and add the top N pieces ofdata into L, and further training m.

This embodiment of this application is further described below in detailwith reference to optional steps:

Step S31: Input the marked data set L including N_(L) images, and trainthe DR classification model f on L.

Step S32: Provide the unmarked data set U including N_(U) images, andinput fundus images (that is, the training pictures) in U into theclassification model f to predict a classification result P, where P isa matrix of N_(u)*N_(c) (where a value of N_(u) is a positive integernot greater than a quantity N_(U) of images), and each dimension in arow vector represents a probability that a current picture belongs tothe N_(i) ^(th) (N_(i) is a constant not greater than a quantity N_(c)of classes) class predicted by the model.

The N_(c) classes herein may be the two pathological types, namely, NPDRand PDR, or may be a sub-divided type.

Step S33: Provide a prediction result P of any fundus image in Uaccording to the prediction result of the model f on the unmarked data,and evaluate the importance of each image by using the following threeindexes.

(1) Uncertainty PA

PA=−Σ _(i=1) ^(N) ^(c) p _(i) log(p _(i))

p_(i) represents the probability that the fundus image belongs to theN_(i) ^(th) class, and may be outputted by the model.

(2) Density d

A feature vector of a fully connected layer of a previous layer of animage classification layer of a fundus image in U may be extracted byusing the classification model f; and such a feature of the image in Uis clustered by using the K-means algorithm, to obtain K clustercenters.

For any fundus image x in U, the density x and a mean cosine distance ofa feature vector corresponding to an image in U can be calculated byusing the following formula:

$d = {\frac{1}{{M\; 1}}{\sum\limits_{x^{\prime} = 1}^{M\; 1}\; \frac{{f_{FC}(x)}{f_{FC}\left( x^{\prime} \right)}}{{{f_{FC}(x)}}{{f_{FC}\left( x^{\prime} \right)}}}}}$

f_(FC) represents a feature of a fully connected layer of the last layerof the extracted image.

(3) Diversity

A feature of a fully connected layer of a last layer of an image in theL is extracted by using the classification model.

Such a feature of the image in the L is clustered by using the K-meansalgorithm, to obtain K cluster centers.

For any image x in U, the diversity thereof can be calculated by usingthe following formula:

$v = {{- \frac{1}{{M\; 2}}}{\sum\limits_{x^{\prime} = 1}^{M\; 2}\; \frac{{f_{FC}(x)}{f_{FC}\left( x^{\prime} \right)}}{{{f_{FC}(x)}}{{f_{FC}\left( x^{\prime} \right)}}}}}$

Step S34: Fuse the three indexes by using the following formula toobtain an importance score s (or referred to as a confidence) of eachimage:

s=PA*d^(w) ¹ +w₂*v, where w₁ and w₂ are weights of indexes or presetparameters. For example, w₁ is 0.5, w₂ is 0.3; w₁ is 0.8, w₂ is 0.1; orw₁ is 0.4, w₂ is 0.6. The values may be adjusted according to actualsituations.

Step S35: Sort images in U according to s, mark the top N images, andupdate U.

Step S36: Add the newly marked images into the training set L, andreturn to step S601 to start a new iteration.

A plurality of iterations may be performed according to requirements,thereby improving recognition accuracy of a neural network model.

After the neural network model is trained, the model may be applied toscenarios shown in FIG. 1, FIG. 3, and FIG. 5. A user captures an imageof a pathological part and uploads the image on a mobile terminal, andreceives a recognition result returned by the neural network model. Forexample, a probability of NPDR is 90%, a probability of PDR is 5%, and aprobability of overall exclusion is 5%

If a DR fundus image classification model (that is, the first neuralnetwork model) is trained by using a supervised learning method, thefollowing defects may be caused:

(1) Performance of the DR classification model depends on a scale ofdata manually marked, and a classification model with high quality needsto be trained by using a large scale of marked data;

(2) That marking personnel may add newly marked data in a process oftraining is not considered in the training method of the DRclassification model, and only a scenario of performing one-timetraining in an unchanged training set is considered.

(3) The DR classification model is in short of a mechanism ofautomatically selecting and providing high-quality and unmarked data forthe marking personnel to generate newly training data.

However, in the embodiments of this application, a large quantity ofmanually marked data with known classification does not need to be usedas training samples, data that enables the model to converge at a higherspeed can be marked through automatic and purposeful selection, and isused for training of a next stage. By using such a method, a quantity ofmarked data needed before convergence of model training can be reducedgreatly, which effectively reduces the costs caused by manually markingdata during training, thereby training a high-quality model with theleast marked data.

For simple descriptions, the foregoing method embodiments are stated asa series of action combinations. However, a person skilled in the artneeds to know that this application is not limited to the sequence ofthe described actions because according to this application, some stepsmay use another sequence or may be simultaneously performed.Secondarily, a person skilled in the art needs to know that theembodiments described in the specification all belong to optionalembodiments and the related actions and modules are not necessary forthis application.

According to the descriptions in the foregoing implementations, a personskilled in the art may clearly learn that the method according to theforegoing embodiments may be implemented by relying on software and anecessary and commonly used hardware platform or by using hardware, butin many cases the former is a better implementation. Based on such anunderstanding, the technical solutions of this application essentiallyor the part contributing to the related art may be implemented in a formof a software product. The computer software product is stored in astorage medium (such as a ROM/RAM, a magnetic disk, or an optical disc)and includes several instructions for instructing a terminal device(which may be a mobile phone, a computer, a server, a network device, orthe like) to perform the methods described in the embodiments of thisapplication.

According to another aspect of the embodiments of this application, animage feature recognition apparatus configured to implement theforegoing image feature recognition method is further provided. FIG. 9is a schematic diagram of an optional image feature recognitionapparatus according to an embodiment of this application, and theapparatus may be applied to a server. As shown in FIG. 9, the apparatusmay include: an obtaining unit 91, a recognition unit 93, and a returnunit 95.

The obtaining unit 91 is configured to obtain a recognition request, therecognition request being used for requesting to recognize an imagefeature in a target picture.

Types of pictures captured by a capturing apparatus include, but are notlimited to: a black and white picture or a color picture obtained byphotographing, an image obtained by computed tomography, an imageobtained by positron emission tomography, an image obtained by magneticresonance imaging, an image obtained by medical ultrasonography, and thelike.

The target picture are captured by photographing a suspected lesion partof a target object. The image feature is a feature of the lesion part,for example, a feature having no retinal neovascularizationcorresponding to a NPDR area, and a feature having retinalneovascularization corresponding to a PDR area.

The recognition unit 93 is configured to recognize the image feature inthe target picture by using a first neural network model, the firstneural network model being obtained by training a parameter in a secondneural network model by using a first training set and a second trainingset, image features of training pictures in the first training set beingmarked, and image features of training pictures in the second trainingset being not marked.

The first neural network model trained by the first training set is usedfor recognizing the image features of the training pictures in thesecond training set, and the training pictures with the image featuresrecognized in the second training set is used for continuing to trainthe first neural network model.

The foregoing second neural network model is a model whose parameter isnot initialized (for example, a deep neural network model). First, thesecond neural network model is trained by using the marked trainingpictures in the first training set, to initialize the parameter of thesecond neural network model, and the parameter is optimized by using theunmarked training pictures in the second training set; and then theforegoing first neural network model is obtained.

If the second neural network model is trained by completely using markedtraining pictures, marking the training pictures is highlytime-consuming because of a relatively large quantity of demandedtraining pictures. However, in the technical solutions of thisapplication, only some training pictures may be marked (that is,pictures in the first training set), and the remaining training picturesare not marked, thereby reducing a workload during model training.

The return unit 95 is configured to return a first recognition result ofthe first neural network model, the first recognition result being usedfor at least indicating the image feature recognized from the targetpicture.

The recognition result herein is closely related to the training of theneural network model. For example, if the training pictures includepositive and negative sample pictures of various types of DR, then therecognition result is a specific type of DR or several types of DR, anda specific type of DR corresponds to a corresponding pathologicalfeature (for example, the NPDR corresponds to having no retinalneovascularization, and the PDR corresponds to having retinalneovascularization). For another example, if the training picturesinclude positive and negative sample pictures of various types ofpneumonia, then the recognition result is a type of pneumonia or severaltypes of pneumonia. The training pictures may alternatively be positiveand negative sample pictures of other pathological types, and detailsare not described herein again.

The obtaining unit 91 in the embodiments may be configured to performstep S202 in this embodiment of this application, the recognition unit93 in this embodiment may be configured to perform step S204 in theembodiments of this application, and the return unit 95 in theembodiments may be configured to perform step S206 in the embodiments ofthis application.

Examples and application scenarios in which the foregoing modules arethe same as those of the corresponding steps, but are not limited to thecontent disclosed in the foregoing embodiments. The foregoing modulesmay be run in the hardware environment shown in FIG. 1 as a part of theapparatus, and may be implemented by software, or may be implemented byhardware.

Through the foregoing modules, the image feature of the target pictureis recognized by using the first neural network model when therecognition request is obtained, and a first recognition result of thefirst neural network model is returned, the first recognition resultbeing used for at least indicating the image feature recognized from thetarget picture. The foregoing neural network model may exist in acomputer device in a form of software, and rapidly show a recognitionresult. If a to-be-recognized image feature (for example, a pathologicalfeature) is DR, a technical problem of low efficiency of screening forDR in the related art may be resolved, thereby improving efficiency ofscreening for DR.

Optionally, the apparatus of this application may further include atraining unit, configured to, before the image feature is recognizedfrom the target picture by using a first neural network model, train thesecond neural network model by using the training pictures in the firsttraining set and the training pictures in the second training setsequentially, and use the trained second neural network model as thefirst neural network model, the training pictures in the second trainingset used for training the second neural network model being marked byusing the image features that are recognized by the second neuralnetwork model in advance.

The training unit may include the following modules:

a first training module, configured to perform training initializationon the parameter in the second neural network model by using thetraining pictures in the first training set, and use the second neuralnetwork model after the parameter initialization as a third neuralnetwork model;

a recognition and marking module, configured to recognize, the imagefeatures of the training pictures in the second training set by usingthe third neural network model, and mark the training pictures in thesecond training set by using second recognition results of the thirdneural network model, the second recognition results being used for atleast indicating the image features recognized from the trainingpictures in the second training set; and

a second training module, configured to re-adjust a parameter in thethird neural network model by continuing to train the third neuralnetwork model by using the marked training pictures in the secondtraining set, and use the third neural network model after the parameterre-adjustment as the first neural network model.

Optionally, the recognition and marking module may include: a searchingsub-module, configured to search all the second recognition results ofthe training pictures in the second training set for a plurality ofthird recognition results with a highest confidence; and a markingsub-module, configured to mark corresponding training pictures in thesecond training set by using the third recognition results.

Optionally, the second recognition results are used for indicating aprobability that the recognized pathological feature (that is, the imagefeature) belongs to each of a plurality of pathological types, and thesearching sub-module may be further configured to:

calculate a confidence s of a second recognition result of a currenttraining picture in the second training set according to the followingformula:

s=u*d ^(w) ¹ +w ₂ *v

u being a parameter that is used for representing importance of thecurrent training picture and that is determined according to aprobability of each type in the second recognition result of the currenttraining picture, d being a density parameter determined according to afeature vector recognized by the third neural network model from thecurrent training picture and a feature vector of the training picturesin the second training set, v being a diversity parameter determinedaccording to the feature vector recognized by the third neural networkmodel from the current training picture and a feature vector of thetraining pictures in the first training set, w₁ and w₂ beingpre-configured parameters; and

obtain a plurality of results with a highest confidence in all thesecond recognition results of the training pictures in the secondtraining set as third recognition results.

Optionally, the return unit is configured to return the firstrecognition result used for representing the pathological type of arecognized image feature and a confidence of the image feature belongingto the pathological type.

Optionally, the return unit is further configured to return a firstrecognition result used for representing the NPDR; and/or return a firstrecognition result used for representing PDR.

If a DR fundus image classification model (that is, the first neuralnetwork model) is trained by using a supervised learning method, thefollowing defects may be caused:

(1) Performance of the DR classification model depends on a scale ofdata manually marked, and a classification model with high quality needsto be trained by using a large scale of marked data;

(2) That marking personnel may add newly marked data in a process oftraining is not considered in the training method of the DRclassification model, and only a scenario of performing one-timetraining in an unchanged training set is considered.

(3) The DR classification model is in short of a mechanism ofautomatically selecting and providing high-quality and unmarked data forthe marking personnel to generate newly training data.

However, in the embodiments of this application, a large quantity ofmanually marked data with known classification does not need to be usedas training samples, data that enables the model to converge at a higherspeed can be marked through automatic and purposeful selection, and isused for training of a next stage. By using such a method, a quantity ofmarked data needed before convergence of model training can be reducedgreatly, which effectively reduces the costs caused by manually markingdata during training, thereby training a high-quality model with theleast marked data.

Examples and application scenarios in which the foregoing modules arethe same as those of the corresponding steps, but are not limited to thecontent disclosed in the foregoing embodiments. The foregoing modulesmay be run in the hardware environment shown in FIG. 1 as a part of theapparatus, and may be implemented by software, or may be implemented byhardware. The hardware environment includes a network environment.

According to another aspect of the embodiments of this application, aserver or a terminal configured to implement the foregoing image featurerecognition method is further provided.

FIG. 10 is a structural block diagram of a server according to anembodiment of this application. As shown in FIG. 10, the server mayinclude: one or more (only one is shown in FIG. 10) processor 1001, amemory 1003, and a transmission apparatus 1005 (such as the transmittingapparatus in the foregoing embodiments). As shown in FIG. 10, the servermay further include an input/output device 1007.

The memory 1003 may be configured to store a software program and amodule, for example, a program instruction/module corresponding to theimage feature recognition method and apparatus in an embodiment of thisapplication, and the processor 1001 performs various functionalapplications and data processing by running a software program and amodule stored in the memory 1003, that is, implementing the foregoingimage feature recognition method. The memory 1003 may include ahigh-speed random memory, and may further include a non-volatile memorysuch as one or more magnetic storage apparatuses, a flash, or anothernon-volatile solid-state memory. In some examples, the memory 1003 mayfurther include memories disposed remote to the processor 1001, andthese remote memories may be connected to the server through a network.Instances of the network include, but are not limited to, the Internet,an intranet, a local area network, a mobile communications network, anda combination thereof.

The transmission apparatus 1005 is configured to receive or send data bymeans of a network, or may further be configured to transmit databetween the processor and the memory. Optional examples of the foregoingnetwork may include a wired network and a wireless network. In anexample, the transmission apparatus 1005 includes a network interfacecontroller (NIC). The NIC may be connected to another network device anda router by using a network cable, so as to communicate with theInternet or the local network. In an example, the transmission apparatus1005 is a radio frequency (RF) module, which communicates with theInternet in a wireless manner.

Optionally, the memory 1003 is configured to store an applicationprogram.

The processor 1001 may invoke, by using the transmission apparatus 1005,the application program stored in the memory 1003, to perform thefollowing steps:

obtaining a recognition request, the recognition request being used forrequesting to recognize an image feature in a target picture;

recognizing the image feature in the target picture by using a firstneural network model, the first neural network model being obtained bytraining a parameter in a second neural network model by using a firsttraining set and a second training set, image features of trainingpictures in the first training set being marked, and image features oftraining pictures in the second training set being not marked; the firstneural network model trained by the first training set being used forrecognizing the image features of the training pictures in the secondtraining set, and the training pictures with the image featuresrecognized in the second training set being used for continuing to trainthe first neural network model; and

returning a first recognition result of the first neural network model,the first recognition result being used for at least indicating theimage feature recognized from the target picture.

The processor 1001 is further configured to perform the following steps

performing training initialization on the parameter in the second neuralnetwork model by using the training pictures in the first training set,and using the second neural network model after the parameterinitialization as a third neural network model;

recognizing, the image features of the training pictures in the secondtraining set by using the third neural network model, and marking thetraining pictures in the second training set by using second recognitionresults of the third neural network model, the second recognitionresults being used for at least indicating the image features recognizedfrom the training pictures in the second training set; and

re-adjusting a parameter in the third neural network model by continuingto train the third neural network model by using the marked trainingpictures in the second training set, and using the third neural networkmodel after the parameter re-adjustment as the first neural networkmodel.

In this embodiment of this application, the image feature of the targetpicture is recognized by using the first neural network model when therecognition request is obtained, and a first recognition result of thefirst neural network model is returned, the first recognition resultbeing used for at least indicating the image feature recognized from thetarget picture. The foregoing neural network model may exist in acomputer device in a form of software, and rapidly show a recognitionresult. If a to-be-recognized image feature (for example, a pathologicalfeature) is DR, a technical problem of low efficiency of screening forDR in the related art may be resolved, thereby improving efficiency ofscreening for DR.

Optionally, for an optional example in this embodiment, reference may bemade to the examples described in the foregoing embodiments, and detailsare not repeated herein.

A person of ordinary skill in the art may understand that the structureshown in FIG. 10 is merely an example, and the server may be a serverdevice such as a smartphone (for example, an Android mobile phone and aniOS mobile phone), a tablet computer, a palmtop computer, mobileInternet devices (MID), and a PAD. FIG. 10 does not limit the structureof the foregoing electronic apparatus. For example, the server mayfurther include more or less components (for example, a networkinterface and a display apparatus) than those shown in FIG. 10, or haveconfiguration different with that shown in FIG. 10.

A person of ordinary skill in the art may understand that all or a partof the steps of the methods of the foregoing embodiments may beimplemented by a program instructing relevant hardware of a serverdevice. The program may be stored in a computer readable storage medium.The storage medium may include a flash disk, a read-only memory (ROM), arandom access memory (RAM), a magnetic disk, a compact disc, or thelike.

An embodiment of this application further provides a storage medium.Optionally, in this embodiment, the storage medium may be configured tostore program code for performing the image feature recognition method.

Optionally, in this embodiment, the storage medium may be located in atleast one of a plurality of network devices in a network shown in theforegoing embodiment.

Optionally, in this embodiment, the storage medium is configured tostore program code used for performing the following steps:

S41: Obtain a recognition request, the recognition request being usedfor requesting to recognize an image feature in a target picture.

S42: Recognize the image feature in the target picture by using a firstneural network model, the first neural network model being obtained bytraining a parameter in a second neural network model by using a firsttraining set and a second training set, image features of trainingpictures in the first training set being marked, and image features oftraining pictures in the second training set being not marked; the firstneural network model trained by the first training set being used forrecognizing the image features of the training pictures in the secondtraining set, and the training pictures with the image featuresrecognized in the second training set being used for continuing to trainthe first neural network model.

S43: Return a first recognition result of the first neural networkmodel, the first recognition result being used for at least indicatingthe image feature recognized from the target picture.

Optionally, the storage medium is further configured to store programcode for performing the following steps:

S51: Perform training initialization on the parameter in the secondneural network model by using the training pictures in the firsttraining set, and use the second neural network model after theparameter initialization as a third neural network model.

S52: Recognize, the image features of the training pictures in thesecond training set by using the third neural network model, and markthe training pictures in the second training set by using secondrecognition results of the third neural network model, the secondrecognition results being used for at least indicating the imagefeatures recognized from the training pictures in the second trainingset.

S53: Re-adjust a parameter in the third neural network model bycontinuing to train the third neural network model by using the markedtraining pictures in the second training set, and use the third neuralnetwork model after the parameter re-adjustment as the first neuralnetwork model.

Optionally, for an optional example in this embodiment, reference may bemade to the examples described in the foregoing embodiments, and detailsare not repeated herein.

Optionally, in this embodiment, the storage medium may include, but isnot limited to, various media such as a USB flash drive, a read-onlymemory (ROM), a random access memory (RAM), a removable hard disk, amagnetic disk, and an optical disc that can store the program code.

The sequence numbers of the foregoing embodiments of this applicationare merely for description purpose and do not indicate the preference ofthe embodiments.

When the integrated unit in the foregoing embodiments is implemented inthe form of a software function unit and sold or used as an independentproduct, the integrated unit may be stored in the foregoingcomputer-readable storage medium. Based on such understanding, thetechnical solutions of this application essentially, or somecontributing to the related art, or all or some of the technicalsolutions may be implemented in a form of a software product. Thecomputer software product is stored in a storage medium and includesseveral instructions for instructing one or more computer devices (whichmay be a personal computer, a server, a network device, or the like) toperform all or some of steps of the methods in the embodiments of thisapplication.

In the foregoing embodiments of this application, descriptions of theembodiments have different emphases, and as for parts that are notdescribed in detail in one embodiment, reference can be made to therelevant descriptions of the other embodiments.

In the several embodiments provided in this application, it isunderstood that the disclosed client may be implemented in othermanners. The apparatus embodiments described above are merely exemplary.For example, the division of the units is merely the division of logicfunctions, and may use other division manners during actualimplementation. For example, a plurality of units or components may becombined, or may be integrated into another system, or some features maybe omitted or not performed. In addition, the coupling, or directcoupling, or communication connection between the displayed or discussedcomponents may be the indirect coupling or communication connection bymeans of some interfaces, units, or modules, and may be electrical or ofother forms.

The units described as separate parts may or may not be physicallyseparate, and parts displayed as units may or may not be physical units,may be located in one position, or may be distributed on a plurality ofnetwork units. Some or all of the units may be selected according toactual requirements to achieve the objectives of the solutions of theembodiments.

In addition, functional units in the embodiments of this application maybe integrated into one processing unit, or each of the units may existalone physically, or two or more units are integrated into one unit. Theintegrated unit may be implemented in the form of hardware, or may beimplemented in the form of software functional unit.

The above descriptions are merely optional implementations of thisapplication, and a person of ordinary skill in the art can make variousimprovements and refinements without departing from the spirit of thisapplication. All such modifications and refinements are also be intendedto be covered by this application.

INDUSTRIAL APPLICABILITY

In the embodiments, a server obtains a recognition request, therecognition request being used for requesting to recognize an imagefeature in a target picture; the server recognizes the image feature inthe target picture by using a first neural network model, the firstneural network model being obtained by training a parameter in a secondneural network model by using a first training set and a second trainingset, image features of training pictures in the first training set beingmarked, and image features of training pictures in the second trainingset being not marked, the first neural network model trained by thefirst training set being used for recognizing the image features of thetraining pictures in the second training set, and the training pictureswith the image features recognized in the second training set being usedfor continuing to train the first neural network model; and the serverreturns a first recognition result of the first neural network model,the first recognition result being used for at least indicating theimage feature recognized from the target picture. A technical problem ofrelatively low efficiency of screening for DR in the related art isresolved, thereby improving the screening efficiency of the DR.

What is claimed is:
 1. An image feature recognition method, comprising:obtaining, by a computing device, a first training set and a secondneural network model, image features of training pictures in the firsttraining set being marked and the second neural network model includingparameters to be trained; training, by the computing device, theparameters of the second neural network model into a first neuralnetwork model using the image features of training pictures in the firsttraining set that have been marked; applying, by the computing device, asecond training set to the first neural network model, image features oftraining pictures in the second training set being not marked, torecognize image features of a subset of the training pictures in thesecond training set and marks the image features of the subset of thetraining pictures in the second training set accordingly; and updating,by the computing device, parameters of the first neural network modelusing the image features of the subset of the training pictures in thesecond training set that have been marked.
 2. The method according toclaim 1, further comprising: before training, by the computing device,the parameters of the second neural network model: performing, by thecomputing device, training initialization on the parameters of thesecond neural network model by using the training pictures in the firsttraining set, and using the second neural network model after theparameter initialization as a third neural network model; and whereinthe applying, by the computing device, the second training set to thefirst neural network model further comprises: recognizing, by thecomputing device, the image features of the training pictures in thesecond training set by using the third neural network model; marking, bythe computing device, the training pictures in the second training setby using second recognition results of the third neural network model,the second recognition results being used for at least indicating theimage features recognized from the training pictures in the secondtraining set; and adjusting, by the computing device, a parameter in thethird neural network model by continuing to train the third neuralnetwork model by using the marked training pictures in the secondtraining set, and using the third neural network model after theparameter adjustment as the first neural network model.
 3. The methodaccording to claim 2, wherein the marking, by the computing device, thetraining pictures in the second training set by using second recognitionresults of the third neural network model comprises: searching, by thecomputing device, all second recognition results of the training picturein the second training set for a plurality of third recognition resultswith a highest confidence; and marking, by the computing device,corresponding training pictures in the second training set by using thethird recognition results.
 4. The method according to claim 3, whereinthe second recognition results are used for indicating a probabilitythat the recognized image feature belongs to each of a plurality ofpathological types, and the searching, by the computing device, allsecond recognition results of the training picture in the secondtraining set for a plurality of third recognition results with a highestconfidence comprises: calculating, by the computing device, a confidences of a second recognition result of a current training picture in thesecond training set according to the following formula:s=PA*d ^(w) ¹ +w ₂ *v PA is a parameter that is used for representingimportance of the current training picture and that is determinedaccording to a probability of each type in the second recognition resultof the current training picture, d being a density parameter determinedaccording to a feature vector recognized by the third neural networkmodel from the current training picture and a feature vector of thetraining pictures in the second training set, v being a diversityparameter determined according to the feature vector recognized by thethird neural network model from the current training picture and afeature vector of the training pictures in the first training set, w₁and w₂ being pre-configured parameters; and obtaining, by the computingdevice, a plurality of results with a highest confidence in all thesecond recognition results of the training pictures in the secondtraining set as the third recognition results.
 5. The method accordingto claim 1, further comprising: obtaining, by the computing device, arecognition request for recognizing an image feature in a targetpicture; applying, by the computing device, the target picture to thefirst neural network model; and returning, by the computing device, afirst recognition result from the first neural network model, the firstrecognition result including at least one indicator of the image featurerecognized from the target picture.
 6. The method according to claim 5,wherein the image feature comprises a pathological feature, and thereturning, by the computing device, a first recognition result of thefirst neural network model comprises: returning, by the computingdevice, the first recognition result used for representing apathological type of a recognized pathological feature and a confidencethat the pathological feature belongs to the pathological type.
 7. Themethod according to claim 5, wherein the image feature comprises apathological feature, and the returning, by the computing device, afirst recognition result of the first neural network model comprises:returning, by the computing device, the first recognition result usedfor representing a non-proliferative diabetic retinopathy; and/orreturning, by the computing device, the first recognition result usedfor representing a proliferative diabetic retinopathy.
 8. A computingdevice, comprising one or more processors and one or more memoriesstoring program units that, when executed by the one or more processors,cause the computing device to perform a plurality of operationsincluding: obtaining, by the computing device, a first training set anda second neural network model, image features of training pictures inthe first training set being marked and the second neural network modelincluding parameters to be trained; training, by the computing device,the parameters of the second neural network model into a first neuralnetwork model using the image features of training pictures in the firsttraining set that have been marked; applying, by the computing device, asecond training set to the first neural network model, image features oftraining pictures in the second training set being not marked, torecognize image features of a subset of the training pictures in thesecond training set and marks the image features of the subset of thetraining pictures in the second training set accordingly; and updating,by the computing device, parameters of the first neural network modelusing the image features of the subset of the training pictures in thesecond training set that have been marked.
 9. The computing deviceaccording to claim 8, wherein the plurality of operations furthercomprise: before training, by the computing device, the parameters ofthe second neural network model: performing, by the computing device,training initialization on the parameters of the second neural networkmodel by using the training pictures in the first training set, andusing the second neural network model after the parameter initializationas a third neural network model; and wherein the applying, by thecomputing device, the second training set to the first neural networkmodel further comprises: recognizing, by the computing device, the imagefeatures of the training pictures in the second training set by usingthe third neural network model; marking, by the computing device, thetraining pictures in the second training set by using second recognitionresults of the third neural network model, the second recognitionresults being used for at least indicating the image features recognizedfrom the training pictures in the second training set; and adjusting, bythe computing device, a parameter in the third neural network model bycontinuing to train the third neural network model by using the markedtraining pictures in the second training set, and using the third neuralnetwork model after the parameter adjustment as the first neural networkmodel.
 10. The computing device according to claim 9, wherein themarking, by the computing device, the training pictures in the secondtraining set by using second recognition results of the third neuralnetwork model comprises: searching, by the computing device, all secondrecognition results of the training picture in the second training setfor a plurality of third recognition results with a highest confidence;and marking, by the computing device, corresponding training pictures inthe second training set by using the third recognition results.
 11. Thecomputing device according to claim 10, wherein the second recognitionresults are used for indicating a probability that the recognized imagefeature belongs to each of a plurality of pathological types, and thesearching, by the computing device, all second recognition results ofthe training picture in the second training set for a plurality of thirdrecognition results with a highest confidence comprises: calculating, bythe computing device, a confidence s of a second recognition result of acurrent training picture in the second training set according to thefollowing formula:s=PA*d ^(w) ¹ +w ₂ *v PA is a parameter that is used for representingimportance of the current training picture and that is determinedaccording to a probability of each type in the second recognition resultof the current training picture, d being a density parameter determinedaccording to a feature vector recognized by the third neural networkmodel from the current training picture and a feature vector of thetraining pictures in the second training set, v being a diversityparameter determined according to the feature vector recognized by thethird neural network model from the current training picture and afeature vector of the training pictures in the first training set, w₁and w₂ being pre-configured parameters; and obtaining, by the computingdevice, a plurality of results with a highest confidence in all thesecond recognition results of the training pictures in the secondtraining set as the third recognition results.
 12. The computing deviceaccording to claim 8, wherein the plurality of operations furthercomprise: obtaining, by the computing device, a recognition request forrecognizing an image feature in a target picture; applying, by thecomputing device, the target picture to the first neural network model;and returning, by the computing device, a first recognition result fromthe first neural network model, the first recognition result includingat least one indicator of the image feature recognized from the targetpicture.
 13. The computing device according to claim 12, wherein theimage feature comprises a pathological feature, and the returning, bythe computing device, a first recognition result of the first neuralnetwork model comprises: returning, by the computing device, the firstrecognition result used for representing a pathological type of arecognized pathological feature and a confidence that the pathologicalfeature belongs to the pathological type.
 14. The computing deviceaccording to claim 12, wherein the image feature comprises apathological feature, and the returning, by the computing device, afirst recognition result of the first neural network model comprises:returning, by the computing device, the first recognition result usedfor representing a non-proliferative diabetic retinopathy; and/orreturning, by the computing device, the first recognition result usedfor representing a proliferative diabetic retinopathy.
 15. Anon-transitory computer readable storage medium, storing a plurality ofprogram units that, when executed by a computing device having one ormore processors, cause the computing device to perform a plurality ofoperations including: obtaining, by the computing device, a firsttraining set and a second neural network model, image features oftraining pictures in the first training set being marked and the secondneural network model including parameters to be trained; training, bythe computing device, the parameters of the second neural network modelinto a first neural network model using the image features of trainingpictures in the first training set that have been marked; applying, bythe computing device, a second training set to the first neural networkmodel, image features of training pictures in the second training setbeing not marked, to recognize image features of a subset of thetraining pictures in the second training set and marks the imagefeatures of the subset of the training pictures in the second trainingset accordingly; and updating, by the computing device, parameters ofthe first neural network model using the image features of the subset ofthe training pictures in the second training set that have been marked.16. The non-transitory computer readable storage medium according toclaim 15, wherein the plurality of operations further comprise: beforetraining, by the computing device, the parameters of the second neuralnetwork model: performing, by the computing device, traininginitialization on the parameters of the second neural network model byusing the training pictures in the first training set, and using thesecond neural network model after the parameter initialization as athird neural network model; and wherein the applying, by the computingdevice, the second training set to the first neural network modelfurther comprises: recognizing, by the computing device, the imagefeatures of the training pictures in the second training set by usingthe third neural network model; marking, by the computing device, thetraining pictures in the second training set by using second recognitionresults of the third neural network model, the second recognitionresults being used for at least indicating the image features recognizedfrom the training pictures in the second training set; and adjusting, bythe computing device, a parameter in the third neural network model bycontinuing to train the third neural network model by using the markedtraining pictures in the second training set, and using the third neuralnetwork model after the parameter adjustment as the first neural networkmodel.
 17. The non-transitory computer readable storage medium accordingto claim 16, wherein the marking, by the computing device, the trainingpictures in the second training set by using second recognition resultsof the third neural network model comprises: searching, by the computingdevice, all second recognition results of the training picture in thesecond training set for a plurality of third recognition results with ahighest confidence; and marking, by the computing device, correspondingtraining pictures in the second training set by using the thirdrecognition results.
 18. The non-transitory computer readable storagemedium according to claim 17, wherein the second recognition results areused for indicating a probability that the recognized image featurebelongs to each of a plurality of pathological types, and the searching,by the computing device, all second recognition results of the trainingpicture in the second training set for a plurality of third recognitionresults with a highest confidence comprises: calculating, by thecomputing device, a confidence s of a second recognition result of acurrent training picture in the second training set according to thefollowing formula:s=PA*d ^(w) ¹ +w ₂ *v PA is a parameter that is used for representingimportance of the current training picture and that is determinedaccording to a probability of each type in the second recognition resultof the current training picture, d being a density parameter determinedaccording to a feature vector recognized by the third neural networkmodel from the current training picture and a feature vector of thetraining pictures in the second training set, v being a diversityparameter determined according to the feature vector recognized by thethird neural network model from the current training picture and afeature vector of the training pictures in the first training set, w₁and w₂ being pre-configured parameters; and obtaining, by the computingdevice, a plurality of results with a highest confidence in all thesecond recognition results of the training pictures in the secondtraining set as the third recognition results.
 19. The non-transitorycomputer readable storage medium according to claim 15, wherein theplurality of operations further comprise: obtaining, by the computingdevice, a recognition request for recognizing an image feature in atarget picture; applying, by the computing device, the target picture tothe first neural network model; and returning, by the computing device,a first recognition result from the first neural network model, thefirst recognition result including at least one indicator of the imagefeature recognized from the target picture.
 20. The non-transitorycomputer readable storage medium according to claim 19, wherein theimage feature comprises a pathological feature, and the returning, bythe computing device, a first recognition result of the first neuralnetwork model comprises: returning, by the computing device, the firstrecognition result used for representing a pathological type of arecognized pathological feature and a confidence that the pathologicalfeature belongs to the pathological type.